Analysis of marketing and entertainment effectiveness using central nervous system, autonomic nervous sytem, and effector data

ABSTRACT

An example system includes an analyzer to identify a degree of amplitude synchrony between a first pattern in a first frequency band in first neuro-response data and a second pattern in a second frequency band in the first neuro-response data, the first neuro-response data gathered via a first modality of collection from a subject while the subject is exposed to media, and modify the degree of amplitude synchrony in response to activity in second neuro-response data, the second neuro-response data gathered via a second modality of collection from the subject while the subject is exposed to the media, the activity corresponding in time to at least a portion of the first pattern or the second pattern. The example system includes an estimator to determine an effectiveness of the media based on the modified degree of amplitude synchrony.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent arises from a continuation of U.S. patent application Ser.No. 15/967,939 now U.S. Pat. No. 10,679,241), which was filed on May 1,2018, which arises from a continuation of U.S. patent application Ser.No. 13/730,511, which was filed on Dec. 28, 2012, which arises from acontinuation of U.S. patent application Ser. No. 12/056,190 (now U.S.Pat. No. 8,484,081), which was filed on Mar. 26, 2008, and claims thebenefit under 35 U.S.C. § 119(e) to U.S. Provisional Patent ApplicationSer. No. 60/908,742, which was filed on Mar. 29, 2007. U.S. patentapplication Ser. No. 15/967,939, U.S. patent application Ser. No.13/730,511, U.S. patent application Ser. No. 12/056,190, and U.S.Provisional Patent Application Ser. No. 60/908,742 are all incorporatedherein by reference in their entireties.

FIELD OF THE DISCLOSURE

The present disclosure relates to the analysis of the effectiveness ofmarketing and entertainment using central nervous system, autonomicnervous system, and effector measurement mechanisms.

BACKGROUND

Conventional systems for measuring the effectiveness of entertainmentand marketing including advertising, brand messages, and productplacement rely on either survey based evaluations or limitedneurophysiological measurements used in isolation. These conventionalsystems provide some useful data but are highly inefficient andinaccurate due to a variety of semantic, syntactic, metaphorical,cultural, social, and interpretative errors and biases. The systems andtechniques themselves used to obtain neurophysiological measurements arealso highly limited.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure may best be understood by reference to the followingdescription taken in conjunction with the accompanying drawings, whichillustrate particular examples.

FIG. 1 illustrates one example of a system for determining theeffectiveness of marketing and entertainment by using central nervoussystem, autonomic nervous system, and effector measures.

FIG. 2 illustrates a particular example of a system having anintelligent protocol generator and presenter device and individualmechanisms for intra-modality response synthesis.

FIG. 3 illustrates a particular example of an intra-modality synthesismechanism for Electroencephalography (EEG).

FIG. 4 illustrates another particular example of synthesis forElectroencephalography (EEG).

FIG. 5 illustrates a particular example of a cross-modality synthesismechanism.

FIG. 6 is one example of a sample flow process diagram showing atechnique for obtaining neurological and neurophysiological data.

FIG. 7 provides one example of a system that can be used to implementone or more mechanisms.

DETAILED DESCRIPTION

Reference will now be made in detail to some specific examples of thedisclosure including the best modes contemplated by the inventors forcarrying out the teachings of the disclosure. Examples of these specificexamples are illustrated in the accompanying drawings. While thedisclosure is described in conjunction with these specific examples, itwill be understood that it is not intended to limit the disclosure tothe described examples. On the contrary, it is intended to coveralternatives, modifications, and equivalents as may be included withinthe spirit and scope of the disclosure as defined by the appendedclaims.

For example, the techniques and mechanisms of the present disclosurewill be described in the context of evaluating entertainment andmarketing effectiveness. However, it should be noted that the techniquesand mechanisms of the present disclosure apply to a variety of differenttypes of entertainment and marketing such as video and audio streams,media advertising, product placement, brand effectiveness, printedadvertisements, etc. It should be noted that various mechanisms andtechniques can be applied to any type of stimuli. In the followingdescription, numerous specific details are set forth in order to providea thorough understanding of the present disclosure. Particular examplesof the present disclosure may be implemented without some or all ofthese specific details. In other instances, well known processoperations have not been described in detail in order not tounnecessarily obscure the present disclosure.

Various techniques and mechanisms of the present disclosure willsometimes be described in singular form for clarity. However, it shouldbe noted that some examples include multiple iterations of a techniqueor multiple instantiations of a mechanism unless noted otherwise. Forexample, a system uses a processor in a variety of contexts. However, itwill be appreciated that a system can use multiple processors whileremaining within the scope of the present disclosure unless otherwisenoted. Furthermore, the techniques and mechanisms of the presentdisclosure will sometimes describe a connection between two entities. Itshould be noted that a connection between two entities does notnecessarily mean a direct, unimpeded connection, as a variety of otherentities may reside between the two entities. For example, a processormay be connected to memory, but it will be appreciated that a variety ofbridges and controllers may reside between the processor and memory.Consequently, a connection does not necessarily mean a direct, unimpededconnection unless otherwise noted.

Overview

Consequently, it is desirable to provide improved methods and apparatusfor measuring and analyzing neurological and neurophysiological data,such as central nervous system, autonomic nervous system, and effectordata obtained during evaluation of the effectiveness of entertainmentand marketing materials.

Central nervous system, autonomic nervous system, and effector data ismeasured and analyzed to determine the effectiveness of marketing andentertainment stimuli. A data collection mechanism including multiplemodalities such as Electroencephalography (EEG), Electrooculography(EOG), Galvanic Skin Response (GSR), etc., collects response data fromsubjects exposed to marketing and entertainment stimuli. A data cleansermechanism filters the response data. The response data is enhanced usingintra-modality response synthesis and/or a cross-modality responsesynthesis.

Examples

Conventional mechanisms for obtaining information about theeffectiveness of various types of stimuli such as marketing andentertainment materials have generally relied on focus groups andsurveys. Subjects are provided with oral and written mechanisms forconveying their thoughts and feelings elicited in response to aparticular advertisement, brand, media clip, etc. These oral and writtenmechanisms provide some limited information on the effectiveness of themarketing and entertainment materials, but have a variety oflimitations. For example, subjects may be unable or unwilling to expresstheir true thoughts and feelings about a topic, or questions may bephrased with built in bias. Articulate subjects may be given more weightthan nonexpressive ones. A variety of semantic, syntactic, metaphorical,cultural, social and interpretive biases and errors prevent accurate andrepeatable evaluation.

Some efforts have been made to use isolated neurological andneurophysiological measurements to gauge subject responses. Someexamples of central nervous system measurement mechanisms includeFunctional Magnetic Resonance Imaging (fMRI) and Electroencephalography(EEG). fMRI measures blood oxygenation in the brain that correlates withincreased neural activity. However, current implementations of fMRI havepoor temporal resolution of few seconds. EEG measures electricalactivity associated with post synaptic currents occurring in themilliseconds range. Subcranial EEG can measure electrical activity withthe most accuracy, as the bone and dermal layers weaken transmission ofa wide range of frequencies. Nonetheless, surface EEG provides a wealthof electrophysiological information if analyzed properly.

Autonomic nervous system measurement mechanisms include Galvanic SkinResponse (GSR), Electrocardiograms (EKG), pupillary dilation, etc.Effector measurement mechanisms include Electrooculography (EOG), eyetracking, facial emotion encoding, reaction time, etc.

Some conventional mechanisms cite a particular neurological orneurophysiological measurement characteristic as indicating a particularthought, feeling, mental state, or ability. For example, one mechanismpurports that the contraction of a particular facial muscle indicatesthe presence of a particular emotion. Others measure general activity inparticular areas of the brain and suggest that activity in one portionmay suggest lying while activity in another portion may suggesttruthfulness. However, these mechanisms are severely limited in theirability to accurately reflect a subject's actual thoughts. It isrecognized that a particular region of the brain can not be mapped to aparticular thought. Similarly, a particular eye movement can not bemapped to a particular emotion. Even when there is a strong correlationbetween a particular measured characteristic and a thought, feeling, ormental state, the correlations are not perfect, leading to a largenumber of false positives and false negatives.

Consequently, the techniques and mechanisms of the present disclosureintelligently blend multiple modes and manifestations of precognitiveneural signatures with cognitive neural signatures and post cognitiveneurophysiological manifestations to more accurately access theeffectiveness of marketing and entertainment materials. In someexamples, autonomic nervous system measures are themselves used tovalidate central nervous system measures. Effector and behaviorresponses are blended and combined with other measures.

Intra-modality measurement enhancements are made in addition to thecross-modality measurement mechanism enhancements. According to variousexamples, brain activity is measured not just to determine the regionsof activity, but to determine interactions and types of interactionsbetween various regions. The techniques and mechanisms of the presentdisclosure recognize that interactions between neural regions supportorchestrated and organized behavior. Thoughts and abilities are notmerely based on one part of the brain but instead rely on networkinteractions between brain regions.

The techniques and mechanisms of the present disclosure furtherrecognize that different frequency bands used for multi-regionalcommunication can be indicative of the effectiveness of stimuli. Forexample, associating a name to a particular face may entail activity incommunication pathways tuned to particular frequencies. According tovarious examples, select frequency bands are analyzed after filtering.The techniques and mechanisms of the present disclosure also recognizethat high gamma band frequencies have significance. Inter-frequencycoupling in the signals have also been determined to indicateeffectiveness. Signals modulated on a carrier wave have also beendetermined to be important in evaluating thoughts and actions. Inparticular examples, the types of frequencies measured are subjectand/or task specific. For example, particular types of frequencies inspecific pathways are measured if a subject is being exposed to a newproduct.

In particular examples, evaluations are calibrated to each subject andsynchronized across subjects. In particular examples, templates arecreated for subjects to create a baseline for measuring pre and poststimulus differentials. According to various examples, stimulusgenerators are intelligent, and adaptively modify specific parameterssuch as exposure length and duration for each subject being analyzed.

Consequently, the techniques and mechanisms of the present disclosureprovide a central nervous system, autonomic nervous system, and effectormeasurement and analysis system that can be applied to evaluate theeffectiveness of materials such as marketing and entertainmentmaterials. Marketing materials may include advertisements, commercials,media clips, brand messages, product brochures, company logos, etc. Anintelligent stimulus generation mechanism intelligently adapts outputfor particular users and purposes. A variety of modalities can be usedincluding EEG, GSR, EKG, pupillary dilation, EOG, eye tracking, facialemotion encoding, reaction time, etc. Individual modalities such as EEGare enhanced by intelligently recognizing neural region communicationpathways. Cross modality analysis is enhanced using a synthesis andanalytical blending of central nervous system, autonomic nervous system,and effector signatures. Synthesis and analysis by mechanisms such astime and phase shifting, correlating, and validating intra-modaldeterminations allow generation of a composite output characterizing theeffectiveness of various stimuli.

FIG. 1 illustrates one example of a system for determining theeffectiveness of marketing and entertainment by using central nervoussystem, autonomic nervous system, and effector measures. According tovarious examples, the neuroanalysis system includes a protocol generatorand presenter device 101. In particular examples, the protocol generatorand presenter device 101 is merely a presenter device and merelypresents stimuli to a user. The stimuli may be a media clip, acommercial, a brand image, a magazine advertisement, a movie, an audiopresentation, particular tastes, smells, textures and/or sounds. Thestimuli can involve a variety of senses and occur with or without humansupervision. Continuous and discrete modes are supported. According tovarious examples, the protocol generator and presenter device 101 alsohas protocol generation capability to allow intelligent customization ofstimuli provided to a subject.

According to various examples, the subjects are connected to datacollection devices 105. The data collection devices 105 may include avariety of neurological and neurophysiological measurement mechanismssuch as EEG, EOG, GSR, EKG, pupillary dilation, eye tracking, facialemotion encoding, and reaction time devices, etc. In particularexamples, the data collection devices 105 include EEG 111, EOG 113, andGSR 115. In some instances, only a single data collection device isused. Data collection may proceed with or without human supervision.

The data collection device 105 collects neuro-physiological data frommultiple sources. This includes a combination of devices such as centralnervous system sources (EEG), autonomic nervous system sources (GSR,EKG, pupillary dilation), and effector sources (EOG, eye tracking,facial emotion encoding, reaction time). In particular examples, datacollected is digitally sampled and stored for later analysis. Inparticular examples, the data collected could be analyzed in real-time.According to particular examples, the digital sampling rates areadaptively chosen based on the neurophysiological and neurological databeing measured.

In one particular example, the neurological and neurophysiologicalanalysis system includes EEG 111 measurements made using scalp levelelectrodes, EOG 113 measurements made using shielded electrodes to trackeye data, GSR 115 measurements performed using a differentialmeasurement system, a facial muscular measurement through shieldedelectrodes placed at specific locations on the face, and a facial affectgraphic and video analyzer adaptively derived for each individual.

In particular examples, the data collection devices are clocksynchronized with a protocol generator and presenter device 101. Thedata collection system 105 can collect data from a single individual (1system), or can be modified to collect synchronized data from multipleindividuals (N+1 system). The N+1 system may include multipleindividuals synchronously tested in isolation or in a group setting. Inparticular examples, the data collection devices also include acondition evaluation subsystem that provides auto triggers, alerts andstatus monitoring and visualization components that continuously monitorthe status of the subject, data being collected, and the data collectioninstruments. The condition evaluation subsystem may also present visualalerts and automatically trigger remedial actions.

According to various examples, the neurological and neurophysiologicalanalysis system also includes a data cleanser device 121. In particularexamples, the data cleanser device 121 filters the collected data toremove noise, artifacts, and other irrelevant data using fixed andadaptive filtering, weighted averaging, advanced component extraction(like PCA, ICA), vector and component separation methods, etc. Thisdevice cleanses the data by removing both exogenous noise (where thesource is outside the physiology of the subject) and endogenousartifacts (where the source could be neurophysiological like musclemovement, eye blinks, etc.).

The artifact removal subsystem includes mechanisms to selectivelyisolate and review the response data and identify epochs with timedomain and/or frequency domain attributes that correspond to artifactssuch as line frequency, eye blinks, and muscle movements. The artifactremoval subsystem then cleanses the artifacts by either omitting theseepochs, or by replacing these epoch data with an estimate based on theother clean data (for example, an EEG nearest neighbor weightedaveraging approach).

According to various examples, the data cleanser device 121 isimplemented using hardware, firmware, and/or software. It should benoted that although a data cleanser device 121 is shown located after adata collection device 105 and before synthesis devices 131 and 141, thedata cleanser device 121 like other components may have a location andfunctionality that varies based on system implementation. For example,some systems may not use any automated data cleanser device whatsoever.In other systems, data cleanser devices may be integrated intoindividual data collection devices.

The data cleanser device 121 passes data to the intra-modality responsesynthesizer 131. The intra-modality response synthesizer 131 isconfigured to customize and extract the independent neurological andneurophysiological parameters for each individual in each modality andblend the estimates within a modality analytically to elicit an enhancedresponse to the presented stimuli. In particular examples, theintra-modality response synthesizer also aggregates data from differentsubjects in a dataset.

According to various examples, the cross-modality response synthesis orfusion device 141 blends different intra-modality responses, includingraw signals and signals output from synthesizer 131. The combination ofsignals enhances the measures of effectiveness within a modality. Thecross-modality response fusion device 141 can also aggregate data fromdifferent subjects in a dataset.

According to various examples, the system also includes a compositeenhanced effectiveness estimator (CEEE) that combines the enhancedresponses and estimates from each modality to provide a blended estimateof the effectiveness of the marketing and entertainment stimuli forvarious purposes. Stimulus effectiveness measures are output at 161.

FIG. 2 illustrates a particular example of a system having anintelligent protocol generator and presenter device (where theintelligence could include a feedback based on prior responses) andindividual mechanisms for intra-modality response synthesis.

According to various examples, the system includes a protocol generatorand presenter device 201. In particular examples, the protocol generatorand presenter device 201 is merely a presenter device and merelypresents preconfigured stimuli to a user. The stimuli may be mediaclips, commercials, brand images, magazine advertisements, movies, audiopresentations, particular tastes, textures, smells, and/or sounds. Thestimuli can involve a variety of senses and occur with or without humansupervision. Continuous and discrete modes are supported. According tovarious examples, the protocol generator and presenter device 201 alsohas protocol generation capability to allow intelligent modification ofthe types of stimuli provided to a subject. In particular examples, theprotocol generator and presenter device 201 receives information aboutstimulus effectiveness measures from component 261.

The protocol generator and presenter device 201 dynamical adapts stimulipresentation by using information from the analysis of attention,analysis of emotional engagement, analysis of memory retention, analysisof overall visual, audio, other sensory effectiveness, and ad, show, orcontent effectiveness, implicit analysis of brand impact, implicitanalysis of brand meaning, implicit analysis of brand archetype,implicit analysis of brand imagery, implicit analysis of brand words,explicit analysis of brand impact, explicit analysis of brand meaning,explicit analysis of brand archetype, explicit analysis of brandimagery, explicit analysis of brand words; analysis of characters in thead, analysis of emotive response to characters in the ad/show/content,analysis of character interaction in the ad/show/content; elicitation ofcore components of the ad/show/content for print purposes, elicitationof core components of the ad/show/content for billboard purposes;elicitation of the ocular metrics like hot-zones in the ad/show/contentby eye dwell time, micro and macro saccade separation, saccadic returnsto points of interest; elicitation of points for product placement,elicitation of points for logo and brand placement; analysis of gameeffectiveness, analysis of product placement in games; analysis ofwebsite effectiveness, webpage dropoff in a site. According to variousexamples, the information is provided by component 261. In particularexamples, the protocol generator and presenter device 201 can itselfobtain some of this information

The protocol generator and presenter device 201 uses a data model alongwith linguistic and image tools like valence, arousal, meaning matchedword/phrase generators, valence and arousal matched image/videoselectors to generate parameters regarding the experiment. In particularexamples, the protocol generator and presenter device 201 may varyindividual presentation parameters like time and duration of theexperiment, the number of repetitions of the stimuli based on signal tonoise requirements, and the number and repetitions of the stimuli forhabituation and wear-out studies, the type and number ofneuro-physiological baselines, and the self reporting surveys toinclude.

In particular examples, the protocol generator and presenter device 201customizes presentations to a group of subjects or to individualsubjects. According to various examples, the subjects are connected todata collection devices 205. The data collection devices 205 may involveany type of neurological and neurophysiological mechanism such as EEG,EOG, GSR, EKG, pupillary dilation, eye tracking, facial emotionencoding, reaction rime, etc. In particular examples, the datacollection devices 205 include EEG 211, EOG 213, and GSR 215. In someinstances, only a single modality is used. In other instances, multiplemodalities are used and may vary depending on the type of effectivenessevaluation. Data collection may proceed without or without humansupervision.

The data collection device 205 automatically collectsneuro-physiological data from multiple sources. This includes acombination of devices such as central nervous system sources (EEG),autonomic nervous system sources (GSR, EKG, pupillary dilation), andeffector sources (EOG, eye tracking, facial emotion encoding, reactiontime). In particular examples, data collected is digitally sampled andstored for later analysis. The digital sampling rates are adaptivelychosen based on the type of neurophysiological and neurological databeing measured.

In particular examples, the system includes EEG 211 measurements madeusing scalp level electrodes, EOG 213 measurements made using shieldedelectrodes to track eye data, GSR 215 measurements performed using adifferential measurement system, and a facial affect graphic and videoanalyzer adaptively derived for each individual.

According to various examples, the data collection devices are clocksynchronized with a protocol generator and presenter device 201. Thedata collection system 205 can collect data from a single individual (1system), or can be modified to collect synchronized data from multipleindividuals (N+1 system). The N+1 system could include multipleindividuals synchronously recorded in a group setting or in isolation.In particular examples, the data collection devices also include acondition evaluation subsystem that provides auto triggers, alerts andstatus monitoring and visualization components that continuously monitorthe status of the data being collected as well as the status of the datacollection instruments themselves. The condition evaluation subsystemmay also present visual alerts and automatically trigger remedialactions.

According to various examples, the system also includes a data cleanserdevice 221. In particular examples, the data cleanser device 221 filtersthe collected data to remove noise, artifacts, and other irrelevant datausing fixed and adaptive filtering, weighted averaging, advancedcomponent extraction (like PCA, ICA), vector and component separationmethods, etc. This device cleanses the data by removing both exogenousnoise (where the source is outside the physiology of the subject) andendogenous artifacts (where the source could be neurophysiological likemuscle movement, eye blinks).

The artifact removal subsystem includes mechanisms to selectivelyisolate and review the output of each of the data and identify epochswith time domain and/or frequency domain attributes that correspond toartifacts such as line frequency, eye blinks, and muscle movements. Theartifact removal subsystem then cleanses the artifacts by eitheromitting these epochs, or by replacing these epoch data with an estimatebased on the other clean data (for example, an EEG nearest neighborweighted averaging approach), or removes these components from thesignal.

According to various examples, the data cleanser device 221 isimplemented using hardware, firmware, and/or software. It should benoted that although a data cleanser device 221 is shown located after adata collection device 205 and before synthesis devices 231 and 241, thedata cleanser device 221 like other components may have a location andfunctionality that varies based on system implementation. For example,some systems may not use any automated data cleanser device whatsoever.In other systems, data cleanser devices may be integrated intoindividual data collection devices.

The data cleanser device 221 passes data to the intra-modality responsesynthesizer 231. The intra-modality response synthesizer is configuredto customize and extract the independent neurological andneurophysiological parameters for each individual in each modality andblend the estimates within a modality analytically to elicit an enhancedresponse to the presented stimuli. In particular examples, theintra-modality response synthesizer also aggregates data from differentsubjects in a dataset. According to various examples, various modulesperform synthesis in parallel or in series, and can operate on datadirectly output from a data cleanser device 221 or operate on dataoutput from other modules. For example, EEG synthesis module 233 canoperate on the output of EOG synthesis module 235. GSR module 237 canoperate on data output from EEG module 233.

According to various examples, the cross-modality response synthesis orfusion device 241 blends different intra-modality responses, includingraw signals as well as signals output from synthesizer 231. Thecombination of signals enhances the measures of effectiveness within amodality. The cross-modality response fusion device 241 can alsoaggregate data from different subjects in a dataset.

According to various examples, the neuro analysis system also includes acomposite enhanced effectiveness estimator (CEEE) that combines theenhanced responses and estimates from each modality to provide a blendedestimate of the effectiveness of the marketing and advertising stimulifor various purposes. Stimulus effectiveness measures are output at 261.A portion or all of the effectiveness measures (intra-modalitysynthesizer, cross modality fusion device, and/or the CEEE) can beprovided as feedback to a protocol generator and presenter device 201 tofurther customize stimuli presented to users 203.

FIG. 3 illustrates a particular example of an intra-modality synthesismechanism. In particular examples, EEG response data is synthesized toprovide an enhanced assessment of marketing and entertainmenteffectiveness. According to various examples, EEG measures electricalactivity resulting from thousands of simultaneous neural processesassociated with different portions of the brain. EEG data can beclassified in various bands. According to various examples, brainwavefrequencies includes delta, theta, alpha, beta, and gamma frequencyranges. Delta waves are classified as those less than 4 Hz and areprominent during deep sleep. Theta waves have frequencies between 3.5 to7.5 Hz and are associated with memories, attention, emotions, andsensations. Theta waves are typically prominent during states ofinternal focus.

Alpha frequencies reside between 7.5 and 13 Hz and typically peak around10 Hz. Alpha waves are prominent during states of relaxation. Beta waveshave a frequency range between 14 and 30 Hz. Beta waves are prominentduring states of motor control, long range synchronization between brainareas, analytical problem solving, judgment, and decision making. Gammawaves occur between 30 and 60 Hz and are involved in binding ofdifferent populations of neurons together into a network for the purposeof carrying out a certain cognitive or motor function, as well as inattention and memory. Because the skull and dermal layers attenuatewaves in this frequency range, brain waves above 75-80 Hz are difficultto detect and are often not used for stimuli response assessment.

However, the techniques and mechanisms of the present disclosurerecognize that analyzing high gamma band (kappa-band: Above 60 Hz)measurements, in addition to theta, alpha, beta, and low gamma bandmeasurements, enhances neurological attention, emotional engagement andretention component estimates. In particular examples, EEG measurementsincluding difficult to detect high gamma or kappa band measurements areobtained, enhanced, and evaluated at 301. At 303, subject and taskspecific signature sub-bands in the theta, alpha, beta, gamma and kappabands are identified to provide enhanced response estimates. Accordingto various examples, high gamma waves (kappa-band) above 80 Hz(typically detectable with sub-cranial EEG and magnetoencephalography)can be used in inverse model-based enhancement of the frequencyresponses to the stimuli.

Various examples of the present disclosure recognize that particularsub-bands within each frequency range have particular prominence duringcertain activities. A subset of the frequencies in a particular band isreferred to herein as a sub-band. For example, a sub-band may includethe 40-45 Hz range within the gamma band. In particular examples,multiple sub-bands within the different bands are selected whileremaining frequencies are band pass filtered. In particular examples,multiple sub-band responses may be enhanced, while the remainingfrequency responses may be attenuated.

At 305, inter-regional coherencies of the sub-band measurements aredetermined. According to various examples, inter-regional coherenciesare determined using gain and phase coherences, Bayesian references,mutual information theoretic measures of independence anddirectionality, and Granger causality techniques of the EEG response inthe different bands. In particular examples, inter-regional coherenciesare determined using fuzzy logic to estimate effectiveness of thestimulus in evoking specific type of responses in individual subjects.

At 307, inter-hemispheric time-frequency measurements are evaluated. Inparticular examples, asymmetries in specific band powers, asymmetries ininter-regional intra-hemispheric coherences, and asymmetries ininter-regional intra-hemisphere inter-frequency coupling are analyzed toprovide measures of emotional engagement.

At 309, inter-frequency coupling assessments of the response aredetermined. In particular examples, a coupling index corresponding tothe measure of specific band activity in synchrony with the phase ofother band activity is determined to ascertain the significance of themarketing and advertising stimulus or sub-sections thereof. At 313, areference scalp power frequency curve is determined using a baselineelectrocorticogram (ECoG) power by frequency function driven model. Thereference scale power frequency curve is compared to an individual scalprecord power by frequency curve to derive scaled estimates of marketingand entertainment effectiveness. According to various examples, scaledestimates are derived used fuzzy scaling.

At 315, an information theory based band-weighting model is used foradaptive extraction of selective dataset specific, subject specific,task specific bands to enhance the effectiveness measure. Adaptiveextraction may be performed using fuzzy scaling. At 321, stimuli can bepresented and enhanced measurements determined multiple times todetermine the variation or habituation profiles across multiplepresentations. Determining the variation and/or habituation profilesprovides an enhanced assessment of the primary responses as well as thelongevity (wear-out) of the marketing and entertainment stimuli. At 323,the synchronous response of multiple individuals to stimuli presented inconcert is measured to determine an enhanced across subject synchronymeasure of effectiveness. According to various examples, the synchronousresponse may be determined for multiple subjects residing in separatelocations or for multiple subjects residing in the same location.

Although a variety of synthesis mechanisms are described, it should berecognized that any number of mechanisms can be applied in sequence orin parallel with or without interaction between the mechanisms. In someexamples, processes 321 and 323 can be applied to any modality. FIG. 4illustrates a particular example of synthesis for Electroencephalography(EEG) data, including ERP and continuous EEG.

ERPs can be reliably measured using electroencephalography (EEG), aprocedure that measures electrical activity of the brain. Although anEEG reflects thousands of simultaneously ongoing brain processes, thebrain response to a certain stimulus may not be visible using EEG. ERPdata includes cognitive neurophysiological responses that manifestsafter the stimulus is presented. In many instances, it is difficult tosee an ERP after the presentation of a single stimulus. The most robustERPs are seen after tens or hundreds of individual presentations arecombined. This combination removes noise in the data and allows thevoltage response to the stimulus to stand out more clearly. In additionto averaging the example includes techniques to extract single trialevoked information from the ongoing EEG.

While evoked potentials reflect the processing of the physical stimulus,event-related potentials are caused by the “higher” processes, thatmight involve memory, expectation, attention, or changes in the mentalstate, among others. According to various examples, evidence of theoccurrence or non-occurrence of specific time domain components inspecific regions of the brain are used to measure subject responsivenessto specific stimulus.

According to various examples, ERP data can be enhanced using a varietyof mechanisms. At 401, event related time-frequency analysis of stimulusresponse—event related power spectral perturbations (ERPSPs)—isperformed across multiple frequency bands such as theta, delta, alpha,beta, gamma and high gamma (kappa). According to various examples, abaseline ERP is determined. At 403, a differential event relatedpotential (DERP) is evaluated to assess stimulus attributabledifferential responses.

At 405, a variety of analysis techniques including principal componentanalysis (PCA), independent component analysis (ICA), and Monte Carlosanalysis can be applied to evaluate an ordered ranking of theeffectiveness across multiple stimuli. In particular examples, PCA isused to reduce multidimensional data sets to lower dimensions foranalysis. ICA is typically used to separate multiple components in asignal. Monte Carlo relies on repeated random sampling to computeresults. According to various examples, an ERP scenario is developed at407 to determine a subject, session and task specific response baseline.The baseline can then be used to enhance the sensitivity of other ERPresponses to the tested stimuli.

At 421, stimuli can be presented and enhanced measurements determinedmultiple times to determine the variation or habituation profiles acrossmultiple presentations. Determining the variation and/or habituationprofiles provides an enhanced assessment of the primary responses aswell as the longevity (wear-out) of the marketing and entertainmentstimuli. At 423, the synchronous response of multiple individuals tostimuli presented in concert is measured to determine an enhanced acrosssubject synchrony measure of effectiveness. According to variousexamples, the synchronous response may be determined for multiplesubjects residing in separate locations or for multiple subjectsresiding in the same location.

A variety of processes such as processes 421, and 423 can be applied toa number of modalities, including EOG, eye tracking, GSR, facial emotionencoding, etc. In addition, synthesis of data from mechanisms such asEOG and eye tracking can also benefit from the grouping objects ofinterest into temporally and spatially defined entities using micro andmacro saccade patterns. Gaze, dwell, return of eye movements toprimarily center around the defined entities of interest and inhibitionof return to novel regions of the material being evaluated are measuredto determine the degree of engagement and attention evoked by thestimulus.

Although intra-modality synthesis mechanisms provide enhancedeffectiveness data, additional cross-modality synthesis mechanisms canalso be applied. FIG. 5 illustrates a particular example of across-modality synthesis mechanism. A variety of mechanisms such as EEG501, Eye Tracking 503, GSR 505, EOG 507, and facial emotion encoding 509are connected to a cross-modality synthesis mechanism. Other mechanismsas well as variations and enhancements on existing mechanisms may alsobe included. According to various examples, data from a specificmodality can be enhanced using data from one or more other modalities.In particular examples, EEG typically makes frequency measurements indifferent bands like alpha, beta and gamma to provide estimates ofeffectiveness. However, the techniques of the present disclosurerecognize that effectiveness measures can be enhanced further usinginformation from other modalities.

For example, facial emotion encoding measures can be used to enhance thevalence of the EEG emotional engagement measure. EOG and eye trackingsaccadic measures of object entities can be used to enhance the EEGestimates of effectiveness including but not limited to attention,emotional engagement, and memory retention. According to variousexamples, a cross-modality synthesis mechanism performs time and phaseshifting of data to allow data from different modalities to align. Insome examples, it is recognized that an EEG response will often occurhundreds of milliseconds before a facial emotion measurement changes.Correlations can be drawn and time and phase shifts made on anindividual as well as a group basis. In other examples, saccadic eyemovements may be determined as occurring before and after particular EEGresponses. According to various examples, time corrected GSR measuresare used to scale and enhance the EEG estimates of effectivenessincluding attention, emotional engagement and memory retention measures.

Evidence of the occurrence or non-occurrence of specific time domaindifference event-related potential components (like the DERP) inspecific regions correlates with subject responsiveness to specificstimulus. According to various examples, ERP measures are enhanced usingEEG time-frequency measures (ERPSP) in response to the presentation ofthe marketing and entertainment stimuli. Specific portions are extractedand isolated to identify ERP, DERP and ERPSP analyses to perform. Inparticular examples, an EEG frequency estimation of attention, emotionand memory retention (ERPSP) is used as a co-factor in enhancing theERP, DERP and time-domain response analysis.

EOG measures saccades to determine the presence of attention to specificobjects of stimulus. Eye tracking measures the subject's gaze path,location and dwell on specific objects of stimulus. According to variousexamples, EOG and eye tracking is enhanced by measuring the presence oflambda waves (a neurophysiological index of saccade effectiveness) inthe ongoing EEG in the occipital and extra striate regions, triggered bythe slope of saccade-onset to estimate the effectiveness of the EOG andeye tracking measures. In particular examples, specific EEG signaturesof activity such as slow potential shifts and measures of coherence intime-frequency responses at the Frontal Eye Field (FEF) regions thatpreceded saccade-onset are measured to enhance the effectiveness of thesaccadic activity data.

GSR typically measures the change in general arousal in response tostimulus presented. According to various examples, GSR is enhanced bycorrelating EEG/ERP responses and the GSR measurement to get an enhancedestimate of subject engagement. The GSR latency baselines are used inconstructing a time-corrected GSR response to the stimulus. Thetime-corrected GSR response is co-factored with the EEG measures toenhance GSR effectiveness measures.

According to various examples, facial emotion encoding uses templatesgenerated by measuring facial muscle positions and movements ofindividuals expressing various emotions prior to the testing session.These individual specific facial emotion encoding templates are matchedwith the individual responses to identify subject emotional response. Inparticular examples, these facial emotion encoding measurements areenhanced by evaluating inter-hemispherical asymmetries in EEG responsesin specific frequency bands and measuring frequency band interactions.The techniques of the present disclosure recognize that not only areparticular frequency bands significant in EEG responses, but particularfrequency bands used for communication between particular areas of thebrain are significant. Consequently, these EEG responses enhance theEMG, graphic and video based facial emotion identification.

FIG. 6 is a flow process diagram showing a technique for obtainingneurological and neurophysiological data. At 601, a protocol isgenerated and stimulus is provided to one or more subjects. According tovarious examples, stimulus includes streaming video, media clips,printed materials, individual products, etc. The protocol determines theparameters surrounding the presentation of stimulus, such as the numberof times shown, the duration of the exposure, sequence of exposure,segments of the stimulus to be shown, etc. Subjects may be isolatedduring exposure or may be presented materials in a group environmentwith or without supervision. At 603, subject responses are collectedusing a variety of modalities, such as EEG, ERP, EOG, GSR, etc. In someexamples, verbal and written responses can also be collected andcorrelated with neurological and neurophysiological responses. At 605,data is passed through a data cleanser to remove noise and artifactsthat may make data more difficult to interpret. According to variousexamples, the data cleanser removes EEG electrical activity associatedwith blinking and other endogenous/exogenous artifacts.

At 611, intra-modality response synthesis is performed to enhanceeffectiveness measures. At 613, cross-modality response synthesis isperformed to further enhance effectiveness measures. It should be notedthat in some particular instances, one type of synthesis may beperformed without performing the other type of synthesis. For example,cross-modality response synthesis may be performed with or withoutintra-modality synthesis. At 615, a composite enhanced effectivenessestimate is provided. At 621, feedback is provided to the protocolgenerator and presenter device for additional evaluations. This feedbackcould be provided by the cross-modality response synthesizer or othermechanisms.

According to various examples, various mechanisms such as the datacollection mechanisms, the intra-modality synthesis mechanisms,cross-modality synthesis mechanisms, etc. are implemented on multipledevices. However, it is also possible that the various mechanisms beimplemented in hardware, firmware, and/or software in a single system.FIG. 7 provides one example of a system that can be used to implementone or more mechanisms. For example, the system shown in FIG. 7 may beused to implement a data cleanser device or a cross-modality responsessynthesis device.

According to particular examples, a system 700 suitable for implementingparticular examples of the present disclosure includes a processor 701,a memory 703, an interface 711, and a bus 715 (e.g., a PCI bus). Whenacting under the control of appropriate software or firmware, theprocessor 701 is responsible for such tasks such as pattern generation.Various specially configured devices can also be used in place of aprocessor 701 or in addition to processor 701. The completeimplementation can also be done in custom hardware. The interface 711 istypically configured to send and receive data packets or data segmentsover a network. Particular examples of interfaces the device supportsinclude host bus adapter (HBA) interfaces, Ethernet interfaces, framerelay interfaces, cable interfaces, DSL interfaces, token ringinterfaces, and the like.

In addition, various very high-speed interfaces may be provided such asfast Ethernet interfaces, Gigabit Ethernet interfaces, ATM interfaces,HSSI interfaces, POS interfaces, FDDI interfaces and the like.Generally, these interfaces may include ports appropriate forcommunication with the appropriate media. In some cases, they may alsoinclude an independent processor and, in some instances, volatile RAM.The independent processors may control such communications intensivetasks as data synthesis.

According to particular examples, the system 700 uses memory 703 tostore data, algorithms and program instructions. The programinstructions may control the operation of an operating system and/or oneor more applications, for example. The memory or memories may also beconfigured to store received data and process received data.

Because such information and program instructions may be employed toimplement the systems/methods described herein, the present disclosurerelates to tangible, machine readable media that include programinstructions, state information, etc. for performing various operationsdescribed herein. Examples of machine-readable media include, but arenot limited to, magnetic media such as hard disks, floppy disks, andmagnetic tape; optical media such as CD-ROM disks and DVDs;magneto-optical media such as optical disks; and hardware devices thatare specially configured to store and perform program instructions, suchas read-only memory devices (ROM) and random access memory (RAM).Examples of program instructions include both machine code, such asproduced by a compiler, and files containing higher level code that maybe executed by the computer using an interpreter.

Although the foregoing disclosure has been described in some detail forpurposes of clarity of understanding, it will be apparent that certainchanges and modifications may be practiced within the scope of theappended claims. Therefore, the present examples are to be considered asillustrative and not restrictive and the disclosure is not to be limitedto the details given herein, but may be modified within the scope andequivalents of the appended claims.

What is claimed is:
 1. A system for transforming neuro-response datacollected using a first modality of collection and a second modality ofcollection into a measure of effectiveness of media, the systemcomprising: an analyzer to: identify a degree of amplitude synchronybetween a first pattern in a first frequency band in firstneuro-response data and a second pattern in a second frequency band inthe first neuro-response data, the first neuro-response data gatheredvia the first modality of collection from a subject while the subject isexposed to the media; and modify the degree of amplitude synchrony inresponse to activity in second neuro-response data, the secondneuro-response data gathered via the second modality of collection fromthe subject while the subject is exposed to the media, the activitycorresponding in time to at least a portion of the first pattern or thesecond pattern; and an estimator to determine an effectiveness of themedia based on the modified degree of amplitude synchrony.
 2. The systemof claim 1, wherein the analyzer is to identify the degree of amplitudesynchrony based on a first amplitude in the first pattern relative to asecond amplitude in the second pattern.
 3. The system of claim 1,wherein the analyzer is to: determine a first change between a firstamplitude in the first frequency band and a second amplitude in thesecond frequency band; determine a second change between a thirdamplitude in the second frequency band a fourth amplitude in the secondfrequency band; and identify the degree of amplitude synchrony based ona coherence between the first change and the second change.
 4. Thesystem of claim 1, wherein the analyzer is to further determine a degreeof phase synchrony between the first pattern in the first frequency bandand the second pattern in the second frequency band and the estimator tois to further determine the effectiveness of the media based on thedegree of phase synchrony.
 5. The system of claim 1, wherein the firstfrequency band has a first frequency range and the second frequency bandhas a second frequency range, the second frequency range different thanthe first frequency range.
 6. The system of claim 1, wherein the firstfrequency band and the second frequency band include the same frequencyrange, the first frequency band gathered from a first hemisphere of abrain of the subject and the second frequency band gathered from asecond hemisphere of the brain.
 7. The system of claim 1, wherein thefirst modality of collection includes electroencephalography and thesecond modality of collection includes galvanic skin response.
 8. Atangible machine readable storage device or storage disc comprisinginstructions that, when executed, cause a machine to at least: identifya degree of amplitude synchrony between a first pattern in a firstfrequency band in first neuro-response data and a second pattern in asecond frequency band in the first neuro-response data, the firstneuro-response data gathered via a first modality of collection from asubject while the subject is exposed to media; modify the degree ofamplitude synchrony in response to activity in second neuro-responsedata, the second neuro-response data gathered via a second modality ofcollection from the subject while the subject is exposed to the media,the activity corresponding in time to at least a portion of the firstpattern or the second pattern; and determine an effectiveness of themedia based on the modified degree of amplitude synchrony.
 9. Thetangible machine readable storage device or storage disk of claim 8,wherein the instructions, when executed, cause the machine to identifythe degree of amplitude synchrony based on a first amplitude in thefirst pattern relative to a second amplitude in the second pattern. 10.The tangible machine readable storage device or storage disk of claim 8,wherein the instructions, when executed, cause the machine to: determinea first change between a first amplitude in the first frequency band anda second amplitude in the second frequency band; determine a secondchange between a third amplitude in the second frequency band a fourthamplitude in the second frequency band; and identify the degree ofamplitude synchrony based on a coherence between the first change andthe second change.
 11. The tangible machine readable storage device orstorage disk of claim 8, wherein the instructions cause the machine to:determine a degree of phase synchrony between the first pattern in thefirst frequency band and the second pattern in the second frequencyband; and determine the effectiveness of the media based the degree ofphase synchrony.
 12. The tangible machine readable storage device orstorage disk of claim 8, wherein the first frequency band has a firstfrequency range and the second frequency band has a second frequencyrange, the second frequency range different than the first frequencyrange.
 13. The tangible machine readable storage device or storage diskof claim 8, wherein the first frequency band and the second frequencyband include the same frequency range, the first frequency band gatheredfrom a first hemisphere of a brain of the subject and the secondfrequency band gathered from a second hemisphere of the brain.
 14. Anapparatus comprising: memory including machine readable instructions;and processor circuitry to execute the instructions to: identify adegree of amplitude synchrony between a first pattern in a firstfrequency band in first neuro-response data and a second pattern in asecond frequency band in the first neuro-response data, the firstneuro-response data gathered via a first modality of collection from asubject while the subject is exposed to media; modify the degree ofamplitude synchrony in response to activity in second neuro-responsedata, the second neuro-response data gathered via a second modality ofcollection from the subject while the subject is exposed to the media,the activity corresponding in time to at least a portion of the firstpattern or the second pattern; and determine an effectiveness of themedia based on the modified degree of amplitude synchrony.
 15. Theapparatus of claim 14, wherein the processor circuitry is to execute theinstructions to identify the degree of amplitude synchrony based on afirst amplitude in the first pattern relative to a second amplitude inthe second pattern.
 16. The apparatus of claim 14, wherein the processorcircuitry is to execute the instructions to: determine a first changebetween a first amplitude in the first frequency band and a secondamplitude in the second frequency band; determine a second changebetween a third amplitude in the second frequency band a fourthamplitude in the second frequency band; and identify the degree ofamplitude synchrony based on a coherence between the first change andthe second change.
 17. The apparatus of claim 14, wherein the processorcircuitry is to execute the instructions to: determine a degree of phasesynchrony between the first pattern in the first frequency band and thesecond pattern in the second frequency band; and determine theeffectiveness of the media based the degree of phase synchrony.
 18. Theapparatus of claim 14, wherein the first frequency band has a firstfrequency range and the second frequency band has a second frequencyrange, the second frequency range different than the first frequencyrange.
 19. The apparatus of claim 14, wherein the first frequency bandand the second frequency band include the same frequency range, thefirst frequency band gathered from a first hemisphere of a brain of thesubject and the second frequency band gathered from a second hemisphereof the brain.
 20. The apparatus of claim 14, wherein the first modalityof collection includes electroencephalography and the second modality ofcollection includes galvanic skin response.